Scaling the Future: Lakehouse Architecture and OpenSource Collaboration
summary
Claims a production lakehouse on Azure using Databricks with Apache Spark, Terraform for IaC, Unity Catalog for governance and DBT for lineage; table formats include Delta and Iceberg. Models use LLMs for data querying and ML for store-level demand prediction; workflows are 70% automated today with humans double-checking and a roadmap to 90–95% autonomy. Reported outcomes: fewer stockouts, lower slow‑moving inventory and measurable cost savings from smarter replenishment.
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chapters (9)
- 0:00AD01 and lakehouse intro
- 3:17Data critical for insights
- 6:04Lakehouse as unified data infra
- 9:14Data-driven decisions multiplier
- 11:01Open-source collaboration benefits
- 13:49AI and data integration insights
- 17:02Smart stocks retail case
- 19:31Future advancements and challenges
- 20:57Q&A on AI deployment
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